Autonomous vehicle safety platform system and method

ABSTRACT

A system  100  for autonomous vehicle operation can include: a low-level safety platform  130 ; and can optionally include and/or interface with any or all of: an autonomous agent  102 , a sensor system, a computing system  120 , a vehicle communication network  140 , a vehicle control system  150 , and/or any suitable components. The system functions to facilitate fallback planning and/or execution at the autonomous agent. Additionally or alternatively, the system can function to transition the autonomous agent between a primary (autonomous) operation mode and a fallback operation mode.

CROSS REFERENCE TO RELATED APPLICATIONS

This this application is a continuation of U.S. application Ser. No.17/550,461, filed, 14 Dec. 2021, which claims the benefit of U.S.Provisional Application Ser. No. 63/125,304, filed 14 Dec. 2020, whichis incorporated herein in its entirety by this reference.

TECHNICAL FIELD

This invention relates generally to the autonomous vehicle field, andmore specifically to a new and useful system and method for autonomousvehicle operation in the autonomous field.

BACKGROUND

Despite numerous recent advancements in the autonomous vehicle field,having a computer for the autonomous vehicle which is deemed auto-gradeand which is resistant to failing has not been achieved. Since computingsystems are integral to operation of autonomous vehicles, failure of thecomputing system can be catastrophic.

While high-level safety platforms (e.g., within a planning module of anautonomous vehicle) can create and implement backup trajectories with acomputing system capable of assuring a minimum risk condition, these areessentially rendered useless in the event that the computing systemloses communication with the vehicle's control system and/or encountersany other emergency scenarios.

Thus, there is a need in the autonomous vehicle field to create animproved and useful low-level safety platform system and method.

BRIEF DESCRIPTION OF THE FIGURES

FIG. 1 is a schematic of a variant of a low-level safety platformsystem.

FIG. 2 is a schematic of a variant of a method for implementing alow-level safety operation of a vehicle.

FIGS. 3A and 3B are a first and a second schematic variation of a set ofinputs and outputs exchanged among the system in a variation of themethod for implementing a low-level safety operation of a vehicle,respectively.

FIG. 4 is a schematic variation of a vehicle in its environment.

FIG. 5 is a schematic representation of a variant of a low-level safetyplatform system.

FIG. 6 is a schematic representation of a variant of a fallback plannerin one variant of the system.

FIG. 7 is a schematic representation of a variant of a low-level safetyplatform system.

FIG. 8 is a schematic of an example of a method for implementing alow-level safety operation of a vehicle.

FIGS. 9A and 9B are a first and second diagrammatic example of safestate identification criteria for navigational edges, respectively.

DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following description of the preferred embodiments of the inventionis not intended to limit the invention to these preferred embodiments,but rather to enable any person skilled in the art to make and use thisinvention.

1. Overview

As shown in FIG. 1, the system 100 for autonomous vehicle operationincludes a low-level safety platform 130. Additionally or alternatively,the system 100 can optionally include and/or interface with any or allof: an autonomous agent 102, a sensor system, a computing system 120, avehicle communication network 140, a vehicle control system 150, and/orany suitable components. The optional sensor system can include: asensor suite 110, a communication interface 160, a set of infrastructuredevices 170, and a teleoperator platform 180. However, the system 100can include any other additional components.

Further additionally or alternatively, the system 100 can include any orall of the components as described in any or all of: U.S. applicationSer. No. 16/514,624, filed 17 Jul. 2019, now issued as U.S. Pat. No.10,564,641; U.S. application Ser. No. 16/505,372, filed 8 Jul. 2019, nowissued as U.S. Pat. No. 10,614,709; U.S. application Ser. No.16/540,836, filed 14 Aug. 2019; and U.S. application Ser. No.16/792,780, filed 17 Feb. 2020; each of which is incorporated herein inits entirety by this reference.

The system 100 functions to facilitate fallback planning and/orexecution at the autonomous agent, in accordance with method S200.Additionally or alternatively, the system can function to transition theautonomous agent between a primary (autonomous) operation mode and afallback operation mode. More preferably, the system 100 can function toenable operation (e.g., safe operation, optimal operation, operationalong a prescribed route, etc.) of a vehicle in an event that acomputing system of the vehicle (e.g., autonomous computing system)fails and/or loses communication abilities (e.g., with a control systemof the vehicle, with actuators such as drive-by-wire actuators of thevehicle, etc.). However, the system can otherwise suitably function.

As shown in FIG. 2, a method S200 for autonomous vehicle operationincludes determining a set of inputs S210; storing any or all of the setof inputs S220; and facilitating operation of an autonomous agent basedon the set of inputs S230.

Further additionally or alternatively, the method 200 can include any orall of the processes described in any or all of: U.S. application Ser.No. 16/514,624, filed 17 Jul. 2019, now issued as U.S. Pat. No.10,564,641; U.S. application Ser. No. 16/505,372, filed 8 Jul. 2019, nowissued as U.S. Pat. No. 10,614,709; U.S. application Ser. No.16/540,836, filed 14 Aug. 2019; and U.S. application Ser. No.16/792,780, filed 17 Feb. 2020; each of which is incorporated herein inits entirety by this reference, or any other suitable processesperformed in any suitable order. The method 200 can be performed with asystem 100 as described above and/or any other suitable system.

1.1 Examples

In variants, the method S200 can include any or all of: retrieving astored input; selecting and/or validating an input; in an event thatcommunication with the computing system is present, transmitting a1^(st) trajectory (equivalently referred to herein as a primarytrajectory) to the communication network; in an event that communicationwith the computing system is lost, transmitting a 2^(nd) trajectory(equivalently referred to herein as a secondary trajectory) to thecommunication network; and/or any other suitable process(es) performedin any suitable order.

In one set of variants, an autonomous fallback method for a vehicle caninclude: receiving input data comprising sensor data from a sensor suiteonboard the autonomous vehicle and an ego-vehicle state of theautonomous vehicle; based on the input data: determining a primary planat an autonomous computing system; and determining a secondary plan,which can include: determining a set of navigational edge candidates,classifying a navigational edge candidate as available, and, based onthe classification of the navigational edge as available, generating thesecond plan based on the ego-vehicle state and the navigational edgecandidate; storing the secondary plan at a memory coupled to an embeddedcontroller; autonomously controlling the vehicle based on the primaryplan; and while autonomously controlling the vehicle based on theprimary plan, determining satisfaction of a trigger condition and, inresponse, autonomously controlling the vehicle based on the secondaryplan with the embedded controller.

In some variants, the autonomous fallback method can further include:while controlling the vehicle based on the primary plan, updating thesecondary plan based on a satisfaction of an expiration condition of thesecondary plan.

In some variants, the autonomous fallback method can further include,while controlling the vehicle based on the secondary plan: estimating anego-vehicle pose using odometry; based on the ego-vehicle pose with aset of secondary sensors, detecting an obstacle along a path of thevehicle corresponding to the secondary plan; and executing a full stopbased on the obstacle detection. In an example, the set of secondarysensors can include time-of-flight sensors of the vehicle sensor suite.

In some variants of the autonomous fallback method, the available stateclassification can be based on a map.

In some variants of the autonomous fallback method, the input datacomprises an environmental representation which identifies a set ofagents (e.g., in an environment of the ego agent), wherein the availablestate classification is based on an occupancy prediction correspondingto each agent of the set. In an example, each agent is assumed to bestatic within a planning horizon. In another example, any or all of theagents are assumed to be dynamically moving with a planning horizon.

In some variants of the autonomous fallback method, the available stateclassification is based on an ego-vehicle dimension and a satisfactionof a minimum passing clearance. In additional or alternative variations,the available state classification is based on a presence or predictedpresence of a neighboring vehicle between the ego vehicle and the state(e.g., a navigational edge associated with the state).

In some variants of the autonomous fallback method, the set ofnavigational edge candidates are determined at a predetermined set ofdistances relative to an ego-vehicle position.

In some variants, the autonomous fallback method can further include:selecting the navigational edge from a plurality of availablenavigational edges based on at least one of: collision constraints,kinematic constraints, and a minimum distance constraint.

In some variants of the autonomous fallback method, the triggercondition is a communication lapse between the autonomous computingsystem and a validation endpoint. In one example, the validationendpoint is a communication network node.

In some variants of the autonomous fallback method, the embeddedcontroller is within a low-level safety platform which iscommunicatively connected to the autonomous computing system and avehicle communication network. In an example, autonomously controllingthe vehicle based on the primary plan includes providing a trajectory toa vehicle control system via the low-level safety platform and thevehicle communication network (e.g., through a vehicle control interfaceas shown in FIGS. 6 and 7). Alternatively, the primary trajectory isprovided to a vehicle control system without passing through thelow-level safety platform.

2. Benefits

The autonomous vehicle safety platform system and method can conferseveral benefits over current systems and methods.

First, variations of the technology can provide increased safety of anautonomous agent as it navigates through its environment, even if the(autonomous) computing system of the agent fails and/or losescommunication with other components of the autonomous agent. In specificexamples, a set of safety trajectories for an environment of the agentcan generated prior to communication/processing failures, which can berelied upon in a fallback mode and/or implemented in case ofcommunication/processing failures (of the autonomous computing system).However, variations can otherwise provide increased safety.

Second, variations of the technology can provide a low-level safetyplatform which can be integrated in a modular fashion into any number ofvehicles, such as autonomous agents. In variants, the low-level safetyplatform can be separately integrated from an autonomous computingsystem, such as added as a retro-fit platform and/or scaled to meet adegree of fallback and/or computing redundancy. However, variants of thesystem can be otherwise suitably integrated into a vehicle and/or thelow-level safety platform can be otherwise implemented.

Third, variations of the technology can provide fallback planningwithout the use of redundant autonomous processing systems, which mayrely on costly and/or resource intensive hardware (e.g.,reserving/requiring additional compute capabilities for failureresponses; increase hardware costs; increasing the amount of advancedprocessing capabilities; etc.). As an example, variants can facilitatevehicle resilience to hardware and/or communication failures whilerelying on only a single advanced processing system (e.g., exactly onecomputing system and/or autonomous computing subsystem therein). Insteadof relying on redundant autonomous processing, variants can utilize afallback controller (e.g., an embedded controller), which can operatewith low-level processing at a low-level safety system (e.g., an exampleis shown in FIG. 5). However, variants can otherwise provide fallbackplanning and/or resilience to communication and/or autonomous processingfailures.

Fourth, variations of the technology can provide technical solutionsnecessarily rooted in computer technology to overcome issuesspecifically arising with computer technology, such as fallbackplanning, redundancy, computing failure resilience of autonomous vehicleplanning/control, and/or other computer technology challenges. Inaddition, or alternative to autonomous vehicle applications, the systemand/or method can be used in any or all of a numerous set ofapplications, such as but not limited to, any or all: militaryapplications, aircraft control, and/or any other suitable applications.

Additionally or alternatively, the system and method can confer anyother benefit(s).

3. System 100

The system 100 functions to facilitate fallback planning and/orexecution at the autonomous agent, in accordance with method S200.Additionally or alternatively, the system can function to transition theautonomous agent between a primary (autonomous) operation mode and afallback operation mode. More preferably, the system 100 can function toenable operation (e.g., safe operation, optimal operation, operationalong a prescribed route, etc.) of a vehicle in an event that acomputing system of the vehicle (e.g., autonomous computing system)fails and/or loses communication abilities. However, the system canotherwise suitably function. Additionally or alternatively, the systemcan function to detect these failures and/or communication losses,retrieve trajectories, store trajectories, generate trajectories, and/orperform any other suitable functions.

The system 100 preferably operates in accordance with the method 200 asdescribed below, but can additionally or alternatively be performed inaccordance with any other suitable method(s). Further additionally oralternatively, the system 100 can function to perform any or all of themethods, processes, embodiments, and/or examples described in any or allof: U.S. application Ser. No. 16/514,624, filed 17 Jul. 2019, now issuedas U.S. Pat. No. 10,564,641; U.S. application Ser. No. 16/505,372, filed8 Jul. 2019, now issued as U.S. Pat. No. 10,614,709; U.S. applicationSer. No. 16/540,836, filed 14 Aug. 2019; and U.S. application Ser. No.16/792,780, filed 17 Feb. 2020; each of which is incorporated herein inits entirety by this reference.

3.1 System: Components

The system 100 preferably includes and/or interfaces with (e.g., isintegrated within) an autonomous vehicle (equivalently referred toherein as an autonomous agent 102 and/or an ego vehicle) (e.g., as shownin FIG. 4). The autonomous agent 102 preferably includes an autonomousvehicle 110 that is preferably a fully autonomous vehicle and/or able tobe operated as a fully autonomous vehicle, but can additionally oralternatively be any semi-autonomous vehicle.

In some examples, for instance, the autonomous agent can be a vehiclethat switches between a semi-autonomous state (e.g., which interfaceswith a human safety driver, which interfaces with a teleoperator, etc.)and a fully autonomous state (or a fully-manned state) and thus, theautonomous agent can have attributes of both a semi-autonomous vehicleand a fully autonomous vehicle depending on the state of the autonomousagent.

In preferred variations, the autonomous vehicle is an automobile (e.g.,car, driverless car, bus, shuttle, taxi, ride-share vehicle, truck,semi-truck, etc.). Additionally or alternatively, the autonomous vehiclecan include any or all of: a watercraft (e.g., boat, water taxi, etc.),aerial vehicle (e.g., plane, helicopter, drone, etc.), terrestrialvehicle (e.g., 2-wheeled vehicle, bike, motorcycle, scooter, etc.),and/or any other suitable vehicle and/or transportation device,autonomous machine, autonomous device, autonomous robot, and/or anyother suitable device.

The autonomous agent preferably includes and/or interfaces with acomputing system 120, wherein the computing system functions to processinformation (e.g., sensor inputs) in order to determine a set of one ormore trajectories (e.g., primary trajectories, secondary trajectories,etc.) for the vehicle. Additionally or alternatively, the computingsystem can function to perform any or all of the processes involved inany or all of: perception, prediction, localization, planning, and/orany other processes involved in operation of the autonomous agent.

The computing system preferably includes an onboard computing systemarranged onboard (e.g., integrated within) the autonomous agent.Additionally or alternatively, the computing system can include any orall of: a remote computing system (e.g., cloud computing system, remotecomputing in communication with an onboard computing system, in place ofan onboard computing system, etc.), a computing system integrated in asupplementary device (e.g., mobile device, user device, etc.), an edgedevice including mobile computing devices, sensor with integratedcomputing units, multiple computing systems (e.g., one which generatesprimary trajectories and another which generates secondary trajectories)and/or any other suitable computing systems and devices. In somevariations, for instance, the autonomous agent is operable incommunication with a remote or disparate computing system that mayinclude a user device (e.g., a mobile phone, a laptop, etc.), a remoteserver, a cloud server, or any other suitable local and/or distributedcomputing system remote from the vehicle. The remote computing systemcan be connected to one or more systems of the autonomous agent throughone or more data connections (e.g., channels), but can alternativelycommunicate with the vehicle system in any suitable manner.

The computing system preferably includes a processing system (e.g.,graphical processing unit or GPU, central processing unit or CPU, or anysuitable processing circuitry) and memory, but can additionally oralternatively include any other suitable components. The memory can beshort term (e.g., volatile, non-volatile, random access memory or RAM,etc.) and/or long term (e.g., flash memory, hard disk, etc.) memory. Insome variations, for instance, the onboard computing system operates tointeract with and/or operably control one or more of the identifiedcomponents or modules described herein. For instance, the onboardcomputing system can function to implement and/or execute computerinstructions for implementing a multi-policy decision making module, asynchronization module, and/or the like. In specific examples, theprocessing system and memory collectively function to dynamically managethe set of policies available to the autonomous agent (e.g., determinedbased on the method 200) in the framework of a multi-policy decisionmaking framework, such as that described in U.S. application Ser. No.16/514,624, filed 17 Jul. 2019, which is incorporated herein in itsentirety by this reference. Additionally or alternatively, theprocessing system and memory, and/or any other suitable components, canbe used for any other suitable functions.

The computing system (e.g., onboard computing system) preferablyfunctions to control the autonomous agent and process sensed data from asensor suite 110 (e.g., a computer vision system, LIDAR, flash LIDAR,wheel speed sensors, GPS, etc.) of the autonomous agent and/or othersensors (e.g., from infrastructure devices) to determine states of theautonomous agent and/or states of agents in an operating environment ofthe autonomous agent. Based upon the states of the autonomous agentand/or agents in the operating environment and programmed instructions,the onboard computing system preferably modifies or controls behavior ofautonomous agent, such as through the selection of a behavioral policy(e.g., which is then used to determine a primary trajectory).Additionally, or alternatively, the computing system can include amulti-policy decision-making module that functions to generatebehavioral policies and select a behavioral policy (e.g., change lanes,merge, maintain current lane, turn left, turn right, pull over, slowdown, speed up, stop at light, stop at stop sign, yield, etc.) that thecomputing system can execute to control a behavior of the autonomousagent (e.g., through the determination of a primary trajectory).

The outputs of the computing system (e.g., and/or autonomous processorstherein) can include preferably behavioral policies and/or operationalinstructions which can include vehicle waypoints (e.g., in a localego-vehicle frame), a target vehicle path (e.g., spatiotemporal controlpath), a polynomial path, trajectories (e.g., in the form of low-levelcontrol commands, such as raw actuator control commands associated withfollow a path/waypoint), but can additionally or alternatively includeany other information. In preferred variants, the computing system isconfigured to output a 1st set of outputs 104 (e.g., a primary plan) anda 2nd set of outputs 106 (e.g., a fallback plan), which can be in thesame format or a different format. As an example, the 1st set of outputs(e.g., a primary plan) can be a trajectory (e.g., path, set ofpositions, low level actuation commands, etc.) and the 2nd set ofoutputs can be in the form of high-level guidance (e.g., waypoints, atarget vehicle path, etc.). However, the computing system can provideany suitable outputs to any suitable endpoints.

The computing system preferably includes, defines, interfaces with,and/or acts as a high-level safety platform 124 (a.k.a. a fallbackplanner), wherein the high-level safety platform is capable of planningand configured to generate a 2nd set of outputs 106 (e.g., as describedbelow), equivalently referred to herein as a fallback plan (e.g.,waypoints, a fallback trajectory), wherein one of the 2nd set of outputscan be utilized for vehicle operation in response to a satisfaction of afallback condition (e.g., event of an emergency; communication failure;failure of the computing system; etc.). Additionally or alternatively,the high-level safety platform can be defined by other components and/orotherwise configured.

In a first set of variations, the computing system includes an onboardgeneral-purpose computer adapted for I/O communication with vehiclecontrol systems and sensor systems but may additionally or alternativelybe any suitable computing device. The onboard computing system ispreferably connected to the Internet via a wireless connection (e.g.,via a cellular link or connection). Additionally, or alternatively, theonboard computing system can be coupled to any number of wireless orwired communication systems.

However, the system can include or be used with any other suitablecomputing system; or can be otherwise suitably implemented.

The system 100 can optionally include a communication interface 160 incommunication with the computing system, which functions to enableinformation to be received at (e.g., from infrastructure devices, from aremote computing system and/or remote server, from a teleoperatorplatform, from another autonomous agent or other vehicle, etc.) andtransmitted from the computing system (e.g., to a remote computingsystem and/or remote server, to a teleoperator platform, to aninfrastructure device, to another autonomous agent or other vehicle,etc.). The communication interface preferably includes a wirelesscommunication system (e.g., Wi-Fi, Bluetooth, cellular 3G, cellular 4G,cellular 5G, multiple-input multiple-output or MIMO, one or more radios,or any other suitable wireless communication system or protocol), butcan additionally or alternatively include any or all of: a wiredcommunication system (e.g., modulated powerline data transfer, Ethernet,or any other suitable wired data communication system or protocol), adata transfer bus (e.g., CAN, FlexRay), and/or any other suitablecomponents. However, the system can include or be used with any othersuitable communication interface; or can otherwise exclude acommunication interface.

The system 100 can optionally include a set of infrastructure devices170 (e.g., as shown in FIG. 4), equivalently referred to herein asroadside units, which individually and/or collectively function toobserve one or more aspects and/or features of an environment andcollect observation data relating to the one or more aspects and/orfeatures of the environment. In preferred variations, the infrastructuredevices additionally function to collect data associated with theobservations and transmit the collected data and/or processedderivatives of the collected data to the autonomous agent. Additionallyor alternatively, the infrastructure devices can function to collect andtransmit data to a teleoperator platform, wherein the teleoperators canuse the data to inform decision making of a teleoperator, such aswhether to include and/or exclude a behavioral policy from considerationby the computing system of the autonomous agent. In a specific example,for instance, an infrastructure device can enable a view around a cornerof the vehicle to be seen, which the agent and/or an operator and/or ateleoperator of the agent can use to enable a turning behavioral policyto be considered by the autonomous agent (by seeing that the road isclear for a turn).

In a first set of variations, for instance, the infrastructure devicesforward the collected observations data to an autonomous vehicle serviceand/or remote platform (e.g., implemented via a network of distributedcomputing systems) that operates to interactively communicate withand/or control one or more functions of the autonomous agent.

The infrastructure devices preferably include devices in an immediateand/or close proximity or within short-range communication proximity toan operating position of an autonomous agent and can function to collectdata regarding circumstances surrounding the autonomous agent and inareas proximate to a zone of operation of the autonomous agent. In someembodiments, the roadside units include one or more of offboard sensingdevices including flash LIDAR, thermal imaging devices (thermalcameras), still or video capturing devices (e.g., image cameras and/orvideo cameras, etc.), global positioning systems, radar systems,microwave systems, inertial measuring units (IMUs), and/or any othersuitable sensing devices or combination of sensing devices.

The infrastructure devices can optionally include computing capabilitiesvia processing circuitry and a communication interface that enables theinfrastructure devices to communicate with any or all of: a computingsystem of the autonomous agent, a remote computing system, ateleoperator platform, and/or any other suitable components orcombination of components.

A zone of operation of the autonomous agent can optionally be defined asa predefined radius (e.g., 100 feet, between 50 feet and 100 feet, lessthan 50 feet, between 100 feet and 200 feet, greater than 200 feet,etc.) along a structured and/or unstructured route of the autonomousagent at any point along the structured route at which the autonomousagent 110 is positioned and/or operating (e.g., driving). In a specificexample of a structured and/or predefined autonomous agent route, theproximate zone of operation of the autonomous agent is 100 feet from oralong any point along the structured route.

A technical benefit achieved by the implementation of the infrastructuredevices can include an ability to observe circumstances (e.g., aroundcorners, down perpendicular streets, etc.) beyond the observable scopeof the autonomous agent, which can subsequently function in the curationof one or more behavioral policies available to the agent. At a giveninstance in time, for example, observations of one or more aspects of agiven environment may be made by an autonomous agent and observations ofone or more different and/or overlapping aspects of the givenenvironment may be made from a different perspective by one or moreinfrastructure devices arranged and operating in the given environment.In such embodiments, the perspective of the infrastructure devices,including the observation data therefrom, may be augmented toobservation data from the perspective of the autonomous agent togenerate a comprehensive perspective of the operating environment of theautonomous agent and/or to provide an additional view to one or moreteleoperators of a teleoperator platform. This can enable improvedpredictions of the operating environment to be made and improvedbehavioral policy decisions (e.g., plans/trajectories) to be selectedand/or executed by the autonomous agent for operating independently (ofan onboard human operator) and safely within the operating environment.

In some variations, the autonomous agent can augment and/or fuse dataderived by an onboard sensor suite 110 (e.g., as described below) withadditional observations from the infrastructure devices (e.g., theroadside units) to improve policy curation and/or trajectorydetermination by the autonomous agent.

Additionally or alternatively, the infrastructure devices can detect andtrack any type or kind of objects in an operating environment, such aswith a video camera or radar. In some variations, for instance, a videocamera can function to provide detection of objects and semanticclassification of the object type and possible intent of an object, suchas a pedestrian that is about to cross a road, or a car that is about tomake a left turn, a driver which is about to open a car door and exittheir vehicle, a bicyclist operating in a bike lane, and/or any othersuitable information.

Further additionally or alternatively, any or all of the infrastructuredevices can include traffic management devices (e.g., traffic sensors,traffic lights, pedestrian lights, etc.) or the like operating in theenvironment that may function to communicate with any or all of: otherinfrastructure devices (e.g., roadside units); directly with theautonomous agent regarding any or all of: data collected and/or sensedby the infrastructure device, regarding an operating state of theinfrastructure device (e.g., red or green traffic light), and/or anyother information; directly with a teleoperator platform; and/or cancommunicate in any other suitable way. In a specific example, a trafficlight can be an infrastructure device in an environment surrounding theautonomous vehicle that may function to communicate directly to theautonomous vehicle or to a roadside unit that may be in operablecommunication with the autonomous vehicle. In this example, the trafficlight can function to share and/or communicate operating stateinformation, such as a light color that the traffic light is projecting,or other information, such as a timing of the light changes by thetraffic light, and/or the like.

However, the system can include or be used with any other suitableinfrastructure devices; or can exclude infrastructure devices.

In preferred variations, the communication interface 160 enables theautonomous agent to communicate and/or exchange data with systems,networks, and/or devices external to the autonomous agent. Thiscommunication interface and/or a separate communication interface canoptionally enable one or more infrastructure devices to communicatedirectly with the autonomous agent and/or with a remote computing systemand/or with a teleoperator platform. Further additionally oralternatively, the communication interface can enable communication tobe established between multiple components of the system and/or agent.The communication interface(s) preferably include one or more of acellular system (or any suitable long-range communication system),direct short-wave radio, or any other suitable short-range communicationsystem.

The communication interface can optionally include, be used inconjunction with, and/or define a vehicle communication network 140,wherein the vehicle communication network functions to enable differentcomponents of the autonomous agent to receive and transmit informationamong each other. The vehicle communication network can be any or allof: manufactured with the vehicle (e.g., an Original EquipmentManufacturer [OEM] communication network), added to the vehicle (e.g.,retrofitted), any combination, and/or otherwise added.

In preferred variations, the vehicle communication network is a vehiclebus, such as, but not limited to, any or all of: a bus (e.g., aController Area Network (CAN) bus, a Local Interconnect Network (LIN)bus, etc.), ethernet, any combination, and/or any other communicationnetwork.

In specific examples, the vehicle communication network includes a CANbus which is in communication with the computing system via thelow-level safety platform.

However, the system can include or be used with any other suitablevehicle communication network and/or can otherwise exclude a vehiclecommunication network.

The system preferably includes and/or interfaces with a sensor suite 110(e.g., computer vision system, LIDAR, RADAR, wheel speed sensors, GPS,cameras, inertial measurement unit [IMU], etc.), wherein the sensorsuite (equivalently referred to herein as a sensor system) is incommunication with the onboard computing system and functions to collectinformation with which to determine one or more trajectories for theautonomous agent. The sensor system (and/or a portion of it) canadditionally or alternatively be in communication with any othercomponents of the system, such as the low-level safety platform (e.g.,such that the fallback controller can receive a simple subset ofinformation to perform odometry and/or simple obstacle detection whileimplementing a secondary trajectory), and/or any other components.Additionally or alternatively, the sensor suite can function to enablethe autonomous agent operations (such as autonomous driving), datacapture regarding the circumstances surrounding the autonomous agent,data capture relating to operations of the autonomous agent, detectingmaintenance needs (e.g., through engine diagnostic sensors, exteriorpressure sensor strips, sensor health sensors, sensor signal qualityself-diagnosis and/or cross-sensor diagnosis, etc.) of the autonomousagent, detecting cleanliness standards of autonomous agent interiors(e.g., internal cameras, ammonia sensors, methane sensors, alcohol vaporsensors), and/or perform any other suitable functions.

The sensor suite 110 preferably includes sensors onboard the autonomousvehicle (e.g., RADAR sensors and/or LIDAR sensors and/or cameras coupledto an exterior surface of the agent, IMUs and/or encoders coupled toand/or arranged within the agent, etc.), but can additionally oralternatively include sensors remote from the agent (e.g., as part ofone or more infrastructure devices, sensors in communication with theagent, etc.), and/or any suitable sensors at any suitable locations.

However, the system can include or be used with any other suitablesensor system(s) and/or sensor suite; or can be otherwise suitablyimplemented.

The system can include and/or interface with a vehicle control system150 including vehicle modules/components which function to effectvehicle motion based on the operational instructions (e.g., plans and/ortrajectories) generated by one or more computing systems and/orcontrollers. Additionally or alternatively, the vehicle control systemcan include, interface with, and/or communicate with any or all of a setelectronic modules of the agent, such as but not limited to, any or allof: component drivers, electronic control units [ECUs], telematiccontrol units [TCUs], transmission control modules [TCMs], antilockbraking system [ABS] control module, body control module [BCM], and/orany other suitable control subsystems and/or modules. In preferredvariations, the vehicle control system includes, interfaces with, and/orimplements a drive-by-wire system of the vehicle. Additionally oralternatively, the vehicle can be operated in accordance with theactuation of one or more mechanical components, and/or be otherwiseimplemented. However, the system can include or be used with any othersuitable vehicle control system; or can be otherwise suitablyimplemented.

The system 100 preferably includes a low-level safety platform 130(e.g., as described below), which functions to process, transmit, and/orstore a set of inputs involved in trajectory selection and/or trajectorygeneration of the vehicle. Additionally or alternatively, the low-levelsafety platform functions to facilitate execution of all or a portion ofthe method S200.

The low-level safety platform is preferably in communication with thecomputing system and the vehicle communication network (e.g., CAN bus,ethernet, etc.). In a first set of variants, the low-level safetyplatform can relay information between the computing system and thevehicle communication network (e.g., from the computing system to thevehicle communication network, from the vehicle communication network tothe computing system, etc.). In a second set of variants, the low-levelsafety platform can transform information (e.g., 2nd set of outputs ofthe computing system) and provide a corresponding set of outputs (e.g.,to the vehicle control system, via the vehicle communication network,via a vehicle control interface, etc.).

Additionally or alternatively, the low-level safety platform can beimplemented in a parallel embodiment (e.g., with 2 parallelcommunications busses) wherein in addition or alternative to relayinginformation and/or arbitrating messages, the low-level safety platformis configured to step to implement a fallback controller (e.g., if thecomputing system is not communicating, wherein the system and theautonomy compute can control the same vehicle and communicate with eachother to detect the need for switching to the backup control, etc.).

In preferred variations, the low-level safety platform can provide oneor more trajectories (and/or any other plan(s)) to the vehiclecommunication network (e.g., in a nominal operating mode and/or in afallback operation mode), wherein the information is subsequentlytransmitted to the vehicle control system for implementation. Theseoutputs can optionally be provided to the vehicle control system througha vehicle control interface (e.g., as shown in FIGS. 6 and 7), whichpreferably includes a software module that serves as the interfacebetween the vehicle control system (e.g., drive-by-wire ECU) and theautonomy stack of the vehicle's computing system (e.g., including thelow-level safety platform, including the high-level safety platform,etc.). Additionally or alternatively, the low-level safety platform candirectly and/or indirectly be in communication with the control systemand/or with any other components of the system (e.g., sensor system,etc.). In some variations, for instance, the low-level safety platformis directly in communication with the vehicle control system. Inadditional or alternative variations, the low-level safety platform isin communication with at least a subset of sensors of the sensor system(e.g., IMU and encoder, only IMU and encoder, only a subset, etc.),wherein the low-level safety platform can use sensor inputs for any orall of: validating one or more received trajectories, generating its owntrajectory in an event of an emergency (e.g., computing system lostconnection), modifying a trajectory (e.g., based on a detected object inthe vehicle's path; implementing a full stop/hard stop in the event ofan obstacle detection); implementing any suitable minimal risk condition(MRC); and/or performing any other suitable functions. Additionally oralternatively, the low-level safety platform can: produce its ownoutputs based on the set of inputs (e.g., by modifying the set of inputsbased on sensor information; generating its own trajectory; determininga full stop trajectory, etc.); select one or more inputs to transmit;store one or more inputs (e.g., in temporary storage, in permanentstorage, etc.); evaluate one or more inputs (e.g., to see if it iswithin a predetermined threshold such as a time and/or distancethreshold since being created); and/or perform any other processes.

The low-level safety platform preferably includes and/or is used inconjunction with a fallback controller (e.g., an example is shown inFIG. 7) which functions to store one or more operation plans (e.g.,fallback plans; 2nd set of outputs 106; received from the computingsystem in a nominal operation mode), and/or retrieve one or moreoperation plans (e.g., from prior storage during a nominal operationmode; when operating in a fallback mode); optionally generateinstructions (e.g., a trajectory, set of waypoints, etc.) based on astored fallback plan; and transmit the instructions associated with thefallback plan. Instructions can herein refer to any or all of: atrajectory, path (e.g., a polynomial path), a set of positions (e.g., aset of waypoints), a set of control commands for actuators of thevehicle (e.g., to reach a destination, to follow the path, to follow theset of points, to stop, to executing an MRC, etc.), and/or any othersuitable information associated with driving the vehicle. The fallbackcontroller is preferably an embedded controller (e.g., embedded withinthe low-level safety platform), but can likewise be an auxiliarycontroller which is separate and distinct from a primary controller ofthe computing system (e.g., packaged within a separate module,communicatively decoupled, relying on a separate processing system,etc.), secondary controller (e.g., redundant with a primary autonomycontroller), and/or any other suitable fallback controller.

In specific examples, the (embedded) fallback controller is notconfigured to perform vehicle path planning or trajectory generation,however the fallback controller can additionally or alternatively beconfigured to generate instructions (e.g., control commands, paths,trajectories, etc.) based on high-level guidance plans (e.g., waypoints,a vehicle path plan, etc.), modify maneuvers/path plans (e.g., execute afull stop in response to obstacle detection), and/or can be configuredto perform any other suitable operations.

Additionally or alternatively, the low-level safety platform and/or oneor more controllers therein can function to implement softwarealgorithms and/or machine learning techniques to assist thefunctionality of the controller, such as featuredetection/classification, obstruction mitigation, route traversal,mapping, sensor integration, ground-truth determination, and/or enableany other suitable functionalities. The controller can include anysuitable software and/or hardware components (e.g., processor andcomputer-readable storage device) utilized for generating controlsignals for controlling the autonomous agent according to a routing goalof the autonomous agent and selected behavioral policies and/or aselected trajectory of the autonomous agent.

Additionally or alternatively, the low-level safety platform can includemultiple (embedded) fallback controllers (e.g., for redundancy, incommunication with each other, etc.). As an example, the low-levelsafety system can include an array of redundant fallback controllersarranged in parallel, which can each be redundantly connected to anysuitable vehicle control endpoints. Additionally or alternatively, thelow-level safety platform can include any suitable controllers (e.g.,microcontrollers), processors (e.g., microprocessors), and/or anysuitable components with any suitable functionality.

The system (and/or low-level safety platform thereof) can optionallyinclude or be used with (e.g., in communication with) a watchdog(a.k.a., verification or validation module) which functions to detectfallback events (e.g., satisfaction of a fallback condition) and, inresponse, transition the autonomous agent and/or low-level safetyplatform to a fallback mode (e.g., and out of a nominal operation mode).Additionally or alternatively, the watchdog can function to validate:functionality of the autonomous operating system, that primarytrajectories/control commands are being communicated via thecommunication system; and/or that guidance plans (e.g., primary and/orfallback) satisfy a set of rules/conditions. However, the system and/orlow-level safety platform can otherwise exclude a watchdog; a watchdogcan be implemented at a separate node of the system (e.g., separate fromboth the computing system and the low-level safety platform), and/or thelow-level safety platform can otherwise transition into a fallback mode.

However, the system can include any other suitable low-level safetyplatform.

Additionally or alternatively, the system can include any or all of: asensor fusion system, a positioning system (e.g., including locationsensors of the sensor system), a guidance system, and/or any suitablecomponents. In some variations, for instance, the sensor fusion systemsynthesizes and processes sensor data and together with a multi-policydecisioning module predicts the presence, location, classification,and/or path of objects and features of the environment of the autonomousagent. In various embodiments, the sensor fusion system may function toincorporate data from multiple sensors and/or data sources, includingbut not limited to cameras, LIDARS, radars, infrastructure devices,remote data feeds (Internet-based data feeds), and/or any number ofother types of sensors.

The positioning system processes sensor data along with other data todetermine a position (e.g., a local position relative to a map, an exactposition relative to lane of a road, vehicle heading, velocity, etc.) ofthe autonomous agent relative to the environment, which can function todetermine what behavioral policies are available to the autonomous agent(e.g., as described below). The guidance system can process sensor dataalong with other data to determine a path for the vehicle to follow.

The system can optionally interface with a teleoperator platform 180,which refers to one or more remote teleoperators and associatedcomponents (e.g., communication interface with autonomous agent,computing system, output devices for displaying information fromautonomous agents and/or infrastructure devices to teleoperators, inputdevices for receiving instructions/commands from teleoperators, etc.).The teleoperator platform can function to receive inputs fromteleoperators, which can be used at least partially in the determinationof the curated behavioral policies for the vehicle.

In a first variation of the system 100, the system includes a low-levelsafety platform in communication with a computing system of the vehicleand a vehicle communication network, wherein the low-level safetyplatform transmits a 1^(st) set of outputs 104 (e.g., a primaryplan/trajectory) from the computing system to the vehicle communicationnetwork, wherein the 1^(st) set of outputs (and/or actuator controlcommands associated with the 1^(st) set of outputs) are subsequentlytransmitted to a drive-by-wire control system of the vehicle, andwherein in an event that a trigger condition occurs (e.g., the low-levelplatform loses communication with the computing system; the low-levelsafety platform detects inconsistencies and/or errors in the trajectoryand/or control commands (or any other instructions) transmitted by thecomputing system; etc.) transmits a retrieved trajectory from a 2nd setof outputs 106 to the vehicle communication network.

In a second variation of the system 100, the system includes a low-levelsafety platform in communication with a computing system of the vehicleand a vehicle communication network, the computing system providestransmits a 1^(st) set of outputs 104 (e.g., a primary plan/trajectory)to the vehicle communication network (e.g., in a nominal operatingmode), wherein the 1^(st) set of outputs (and/or actuator controlcommands associated with the 1^(st) set of outputs) are subsequentlytransmitted to a drive-by-wire control system of the vehicle (e.g.,occurring in a primary operation mode), and wherein in an event that atrigger condition occurs (e.g., the low-level platform losescommunication with the computing system; the low-level safety platformdetects inconsistencies and/or errors in the trajectory and/or controlcommands transmitted by the computing system; etc.) transmits aretrieved trajectory (or any other instructions) from a 2nd set ofoutputs 106 to the vehicle communication network (e.g., occurring in asecondary operation mode, equivalently referred to herein as a fallbackoperation mode).

In specific examples, the low-level safety platform includes one or moreembedded controllers.

Additionally or alternatively, the system 100 can include any othersuitable components.

4. Method

As shown in FIG. 2, a method S200 for autonomous vehicle operationincludes determining a set of inputs S210; storing any or all of the setof inputs S220; and facilitating operation of an autonomous agent basedon the set of inputs S230. The method functions to facilitate control ofan autonomous agent and/or fallback operation of the autonomous agentmode. More preferably, the system 100 can function to enable operationof a vehicle in an event that a computing system of the vehicle (e.g.,autonomous computing system) fails and/or loses communication abilities.However, the method can otherwise suitably function.

In variants, the method S200 can additionally or alternatively includeany or all of: retrieving a stored input; selecting and/or validating aninput; in an event that communication with the computing system ispresent, transmitting a 1^(st) output (e.g., 1^(st) trajectory) to thecommunication network; in an event that communication with the computingsystem is lost, transmitting a 2^(nd) output (e.g., set of actuatorcontrol commands, 2^(nd) trajectory, set of waypoints, any otherinstructions, etc.) to the communication network; and/or any othersuitable process(es) performed in any suitable order. Furtheradditionally or alternatively, the method 200 can include any or all ofthe processes described in any or all of: U.S. application Ser. No.16/514,624, filed 17 Jul. 2019, now issued as U.S. Pat. No. 10,564,641;U.S. application Ser. No. 16/505,372, filed 8 Jul. 2019, now issued asU.S. Pat. No. 10,614,709; U.S. application Ser. No. 16/540,836, filed 14Aug. 2019; and U.S. application Ser. No. 16/792,780, filed 17 Feb. 2020;each of which is incorporated herein in its entirety by this reference,or any other suitable processes performed in any suitable order. Themethod S200 can be performed with a system 100 as described above and/orany other suitable system.

The method S200 is preferably performed continuously and/or repeatedlythroughout operation of the autonomous agent, such as at every electionstep (e.g., every decision-making step, at a predetermined frequencybetween once per second and 100 times per second, between 20 and 30times per second, less than once per second, greater than 100 times persecond, etc.) and/or every control step (e.g., each actuator controloutput generation step) of the autonomous agent. Additionally oralternatively, the method S200 and/or any sub-elements therein can beperformed at a predetermined frequency; in response to a trigger (e.g.,detection of a change in autonomous agent environment, detection of achange in operating conditions of an autonomous agent, based onteleoperator input, etc.); at random intervals of time; and/or at anyother suitable times.

4.1 Method—Determining a Set of Inputs S210

The method 200 includes determining a set of inputs S210, whichfunctions to determine inputs for vehicle control; and/or functions todetermine information with which to generate and/or select instructions(e.g., a trajectory, path, set of waypoints, control commands, etc.) forthe agent.

S210 is preferably performed continuously throughout the method 200(e.g., continuously throughout the agent's traversal of a route), butcan additionally or alternatively be performed at any suitable time(s)(e.g., as described above). Each of set of inputs are preferablyreceived at the low-level safety platform and generated at the computingsystem, but can additionally or alternatively be received at anysuitable component(s) (e.g., the computing system) and/or generated byany suitable component(s) (e.g., the low-level safety platform).

In variants, determining a set of inputs S210 can include: determining aprimary plan; determining a fallback plan; and determining a set ofsensor inputs.

S210 can include determining a primary plan, which can includeoptionally generating a primary plan with the computing system and/orreceiving the primary plan at the low-level safety platform (e.g., asthe 1^(st) set of outputs 104 of the computing system).

The primary plan can include and/or be equivalently referred to hereinas the 1^(st) set of outputs 104, wherein the primary plan can includeand/or represent any or all of: the trajectory (or any otherinstructions) determined for the agent to traverse a selected route forthe agent (e.g., operational trajectory; operational waypoints), vehiclecontrol commands (e.g., associated with a trajectory of the 1^(st) setof trajectories), and/or any other suitable instructions or outputs. Theselected primary plan is preferably along a predetermined route (e.g., afixed route), such as in use cases wherein the ego vehicle is configuredto drive along one or more fixed routes (e.g., to drive passengers alonga shuttle route), but can additionally or alternatively be a dynamicallydetermined route and/or any combination.

The primary plan is preferably received at the vehicle control system tooperate the vehicle as a series of outputs (e.g., control commandsdetermined in accordance with the trajectory) from the computing systemand/or vehicle communication network (e.g., as they are generated, at apredetermined frequency, at each election cycle, etc.), wherein each ofthe outputs represents and/or is associated with the optimal and/orroutine trajectory for the agent (e.g., based on the route of the agent;as determined by the autonomous computing system). Additionally oralternatively, the primary plan can optionally be received at thelow-level safety platform (e.g., prior to being sent to the vehiclecontrol system).

The primary plan is preferably determined continuously (e.g., at apredetermined frequency, at each election cycle, as it is generated,etc.) in a nominal operating mode (e.g., at the computing system, at thelow-level safety platform; while the low-level safety platform is incommunication with the computing system; while the computing system isoperational), but can additionally or alternatively be determined and/orreceived at any suitable time(s) and/or in response to any suitabletriggers.

The primary plan is preferably determined as a 1^(st) set of outputs(and/or actuator control commands associated with the 1^(st) set ofoutputs) of the computing system based on inputs from the system and anyor all of: sensor input from the set of sensors (e.g., sensor suite), aselected route for the agent, a map (e.g., indicate the selected route,indicating other available routes, indicating all possible routes,etc.), a behavioral policy (e.g., as determined in accordance with amulti-policy decision making module), and/or any other suitable inputs.Additionally or alternatively, the primary plan can be determined basedon the ego-vehicle state estimate and a (current) environmentalrepresentation (e.g., which identifies a set of agents and/or objects inthe environment of the autonomous agent) of the computing system and/oran autonomous computing submodule therein. Additionally oralternatively, the 1^(st) set of outputs can be otherwise determinedbased on any suitable inputs. As an example, the primary plan can bedetermined in accordance with the systems and/or methods as described inU.S. application Ser. No. 16/514,624, filed 17 Jul. 2019, and U.S.application Ser. No. 16/792,780, filed 17 Feb. 2020, each of which isincorporated herein in its entirety by this reference.

In a preferred example, the primary plan is not sent to the low-levelsafety platform.

In an alternative example, S210 can include receiving the primary planat the low-level safety platform.

However, a primary plan can be otherwise suitably determined as a partof S210.

S210 can include determining a fallback plan (equivalently referred toherein as a secondary plan), which can be utilized in the event that aprimary plan is unavailable and/or in response to detection of afallback event. The fallback plan is preferably generated at ahigh-level safety platform of the computing system (e.g., at a fallbackplanner of the computing system) and provided (e.g., as a 2^(nd) set ofoutputs of the computing system wherein the 1^(st) set of outputs arenot provided, as a 2^(nd) set of outputs of the computing systemswherein the 1^(st) set of outputs are also provided, etc.) to thelow-level safety platform. The fallback plan is preferably determinedcontemporaneously with (e.g., in parallel, in series, in an overlappingtime period with, within a predetermined time threshold of generation,based on the same set of inputs received at the computing system, etc.)the primary plan, such that either of a primary plan (and/or acorresponding operational trajectory) and a fallback plan (and/orcorresponding fallback trajectory) could be implemented for the agent atan associated time and/or range of times. Additionally or alternatively,the fallback plan can be determined periodically (e.g., at the samefrequency as the primary plan, at a different frequency relative to theprimary plan), synchronously with the primary plan, asynchronously withthe primary plan, and/or with any other suitable timing. The fallbackplan can optionally be received at the low-level safety platformcontemporaneously with (e.g., in parallel with; in quick successionwith; within a predetermined time threshold of less than 30 seconds, 20seconds, 10 seconds, 5 seconds, 1 second, 0.5 seconds, 100 milliseconds,50 milliseconds, 10 milliseconds, 1 millisecond, etc.) with anassociated trajectory of the 1^(st) set of outputs (e.g., in the samedata packet), but can additionally or alternatively be received at thelow-level safety platform at any suitable times. Alternatively, thefallback plan can be received absent of the primary plan at thelow-level safety platform.

In variants, determining the fallback plan can include: determining aset of navigational edges; classifying a navigational edge; and based onthe classification of the navigational edge, generating the fallbackplan based on the ego-vehicle state and the navigational edge.

A navigational edge (equivalently referred to herein as a state and/orsafe state) preferably refers to a location (e.g., as prescribed at amap) at which the ego vehicle can come to a stop (e.g., safely come to astop at, come to a stop at for at least a predetermined amount of time,re-enter the road from after being stopped, safely release passengersat, etc.) and/or otherwise occupy. Navigational edges can bepredetermined (e.g., at known static positions on a map), dynamicallydetermined (e.g., at fixed distances from the vehicle, based on acurrent environmental representation of a computing system; dynamicallygenerated using a neural network, etc.), received from a remote endpoint(e.g., received from a teleoperator platform, etc.), determinedaccording to a set of predetermined rules/heuristics, and/or otherwisesuitably determined. Navigational edges are preferably navigationaltargets (e.g., point, region, line segment, etc.) along infrastructureboundaries of the environment (e.g., sides of a current lane of thevehicle, along a shoulder of a highway, apex of an intersection corner,a driveway or side street intersecting a current road, parking lots,parking spots, etc.), but can be any other suitable edges or targets. Ina specific example, navigational edges can be evaluated at the edges ofa current lane and/or roadway at a set of predetermined distances fromthe vehicle, which can reduce the required computation by minimizing asearch space.

Navigational edges can be scored and/or classified (e.g., tagged asavailable) based on a current environmental representation and/or thecurrent vehicle state. As an example, navigational edges can beconsidered “available” if the vehicle can slow, halt, and/or enter aholding pattern at the navigational edge without excessively impedingtraffic (e.g., fully blocking a lane, such that cars can pass on oneand/or both sides, braking in excess of a predetermined threshold,etc.). As an example, a navigational edge may be rejected and/orclassified as “unavailable” if it is within an intersection (and/orwithin a predetermined minimum distance relative to an intersection,within “d_(intersection)” as shown in FIG. 9A, etc.); if another agentwill occupy it (e.g., has intent on a corresponding region of roadway);if it is within and/or is within a minimum distance of (e.g., within anaverage vehicle's stopping distance of, within a monitored vehicle'sstopping distance, within a length of vehicle, within between 10 and 30feet, within 50 feet, within a stopping distance calculated based on theroad's speed limit and a most extreme braking acceleration, within“d_(aftercrosswalk)” or “d_(beforecrosswalk)” as shown in FIG. 9A, etc.)a crosswalk (e.g., such that another vehicle approaching the crosswalkwould have limited visibility of the crosswalk, an example is shown inFIG. 9B); if it is within and/or within a threshold distance (e.g.,within between 10 and 30 feet, within 50 feet, etc.) of a railroadcrossing or other landmark (e.g., bus stop); if the ego vehicle oranother vehicle would have to exceed a braking threshold and/or stoppingdistance in order to safely maneuver the environment; if another vehicle(e.g., another autonomous vehicle) is occupying or planning to occupythe edge and/or region; and/or if it does not satisfy a minimum passingclearance.

Navigational edges can be further classified and/or selected based on aclassification model (e.g., heuristic classifier, decision tree, etc.)and/or using a set of multi-policy evaluations, statics constraints,dynamic constraints, collision constraints (e.g., one or more agents inthe environmental representation have intent on the navigational edgeand/or are associated with a state estimate that will likely result in acollision; one or more objects intersects the), kinematic constraints ofthe vehicle (e.g., actuator limitations), a minimum distance constraint(e.g., proximity to the vehicle; as a function of current vehiclevelocity, etc.), and/or any other suitable evaluations/metrics.

In variants, an example of which is shown in FIG. 6, a fallback plannerof the high-level safety platform can classify states as available basedon an initial available state classification (e.g., which assumes otheragents in the environment are static within a planning horizon (e.g.,between generating the fallback plan and executing a correspondingmaneuver, etc.), generates a fallback plan (e.g., as a polynomial path,set of waypoints, etc.) for each navigational edge (e.g., a polynomialpath between the current vehicle pose which terminates at thenavigational edge), and then selects a fallback plan based on a dynamiccomparison of the resulting maneuvers/trajectories. As an example, thefallback planner can select the fallback plan which does not exceed amaximum braking constraint and/or a maximum steering constraint.

The fallback plan (e.g., generated by the computing system) can includehigh-level guidance, such as waypoints, kinematic trajectories, locationtargets, paths (e.g., smooth, polynomial paths, etc.); low-level controlcommands (e.g., trajectories, actuator control instructions, etc.);and/or any other suitable information. In a preferred set of variations,the fallback plan includes a set of waypoints which are determined(e.g., at the fallback plan evaluation block of FIG. 6) based on apolynomial fallback trajectory designed to connect the vehicle's currentposition to an identified navigational edge. In a set of specificexamples of this preferred set of variations, the primary plan is of adifferent data type than the fallback plan (e.g., such as a trajectoryand/or path rather than a set of waypoints). Additionally oralternatively, the primary plan and fallback plan can be of the samedata type(s).

In a specific example, a fallback plan (fallback maneuver) can begenerated as a sequence of polynomials (e.g., 7^(th) order polynomials,less than 7^(th) order polynomials, greater than 7^(th) orderpolynomials, between 1^(st) and 10^(th) order polynomials, between5^(th) and 10^(th) order polynomials, etc.) to pull over to the nearsideedge of an available state, wherein the nearside is the side (left orright) that has the same traffic direction as the ego vehicle.

In variants, the fallback plan can optionally include or be associatedwith an expiration parameter and/or condition which prescribes a limiteduse for implementing the fallback plan (e.g., safety trajectory, controlcommands associated with a safety trajectory, etc.). This preferablyfunctions to prevent the use of an output which does not accuratelyrepresent the current environment of the user (e.g., in an event thatthe computing system failed too long ago). Additionally oralternatively, the expiration parameter can perform any other suitablefunctions. The expiration parameter preferably includes and/orprescribes a threshold time and/or distance in which the output of the2^(nd) set of outputs (equivalently referred to herein as a 2^(nd)output) can be used (e.g., from the time of generation, from the time ofreceipt, etc.), but can additionally or alternatively prescribe multipleparameters and/or any other suitable parameters. Alternatively, thefallback plan can otherwise exclude an expiration parameter (e.g., suchas in cases where the fallback plan is replaced with and/or updated viaa subsequent determination of a fallback plan at a later timestep;etc.).

However, the fallback plan can be otherwise suitably determined.

S210 can optionally include determining a set of sensor inputs which canbe used to validate an operational plan (e.g., primary plan and/orfallback plan) and/or facilitate guidance of the autonomous agentaccording to a fallback plan. Sensor inputs are preferably generated bythe sensor suite and received at the low-level safety platform (e.g.,directly; byway of the vehicle communication network; without passingthrough the computing system; etc.).

The set of sensor inputs can be used for any or all of: implementingand/or validating a trajectory (e.g., check that the safety trajectorydoes not collide with any objects), selecting a trajectory, determininga new trajectory (e.g., when the available trajectories is an emptyset), and/or can function to perform any other suitable processes. Theset of sensor inputs received at the low-level safety platform arepreferably received from a subset of the total set of sensors (e.g.,only encoders and IMUs, only a particular view of sensors such as aparticular subset of LIDAR sensors, etc.), but can additionally oralternatively be received from the entire sensor system (e.g., allsensors, all types of sensors, upon redirection of the set of sensorinputs from the computing system to the low-level safety platform,etc.), and/or any other sensors. In a specific example, the set ofsensor inputs received by the low-level safety platform can include:odometry sensor inputs (e.g., GPS velocity and/or inertial sensormeasurements), time-of-flight sensor inputs (e.g., Lidar, radar,“bumper” sensors, etc.), and/or any other suitable inputs. Furtheradditionally or alternatively, the low-level safety platform can operatein absence of sensor information (e.g., where feedback control isdelegated to downstream control endpoints and/or compute nodes, etc.).The set of sensor inputs can be received independently of the fallbackplan (e.g., continuously), with the fallback plan, in response to atrigger (e.g., entering a fallback operation mode), continuously, and/orat any other suitable times (e.g., as described above).

In preferred variations, the low-level safety platform receives inputsfrom sensors configured to determine a position (e.g., location, pose,etc.) of the agent, such as inputs from one or more encoders and/orinertial measurement units (IMUs).

In a first variation, S210 includes receiving, at the low-level safetyplatform: a fallback plan (e.g., waypoints and/or corresponding fallbacktrajectory), and optionally one or more sensor inputs.

In a second variation, S210 includes receiving, at the low-level safetyplatform: a primary plan (e.g., operation trajectory) along with afallback plan (e.g., waypoints and/or corresponding fallbacktrajectory), and optionally one or more sensor inputs.

In variants, the inputs can additionally include an ego-vehicle poseestimate (e.g., localized ego-vehicle reference frame) and/or anenvironmental representation (e.g., a set of agents/objects; inego-vehicle reference frame) as determined by the computing system.

However, inputs can be otherwise suitably determined and/or received.

4.2 Method—Storing any or all of the Set of Inputs S220

The method S200 can include storing any or all of the set of inputsS220, which functions to maintain a persistently available fallback plan(and/or a fallback trajectory associated therewith) which can beimplemented in an event that the computing system fails.

S220 preferably includes storing the fallback plan, but can additionallyor alternatively include storing any or all of the other inputs. Thefallback plan is preferably stored within a memory of the low-levelsafety system and/or a local memory of the system (e.g., which isaccessible without reliance upon the primary computing system), howeverthe fallback plan can additionally or alternatively be stored and/orvalidated remotely, and/or can be otherwise suitably stored.

In a first set of variants, the expiration parameter is furtherpreferably stored with its associated fallback plan (e.g., actuatorcontrol commands, safety trajectory, etc.) but can additionally oralternatively not be stored, separately stored, and/or otherwise stored.

In a second set of variants, the fallback plan can be updated and/orreplaced upon receipt and/or validation of a subsequent fallback plan(e.g., from the computing system and/or communication interface).

The fallback plan (and/or any other inputs) is preferably temporarilystored (e.g., cached, in short-term storage), but can additionally oralternatively be permanently stored, transmitted to a storage site(e.g., remote server), and/or otherwise stored and/or used. S220 canoptionally include deleting and/or replacing previous plans/trajectoriesof the 2nd set of outputs, such as based on any or all of: apredetermined number of outputs (e.g., 2, 3, 4, 5, more than 5, etc.); apredetermined storage amount; a replacement of each previous 2^(nd)output with a new 2^(nd) output; expiry of the expiration parameter;and/or based on any other suitable information. In some variations, S220includes checking (e.g., routinely checking, checking with each receiptof the safety trajectory, etc.) any or all of the stored expirationparameters for satisfaction of a set of one or more threshold conditions(e.g., upper time threshold, upper distance threshold, odometryuncertainty threshold, etc.), wherein the associated 2^(nd) outputs canbe deleted and/or removed from consideration in subsequent processes ofthe method in an event that the threshold conditions are satisfied.

In a first variation, S220 includes storing each of the 2^(nd) set ofoutputs in storage associated with the low-level safety platform (e.g.,storage of an embedded controller), wherein previous versions of the2^(nd) set of outputs can optionally be removed from storage in responseto one or more triggers.

However, inputs can be otherwise suitably stored.

4.3 Method—Facilitating Operation an Autonomous Agent Based on the Setof Inputs S230

The method S200 can include facilitating operation of an autonomousagent based on the set of inputs S230, which functions to enableoperation of the autonomous agent which can be resilient in all ornearly all types of agent failure, such as failure of and/or lostcommunication with a computing system of the autonomous agent.Additionally or alternatively, S230 can function to enable theautonomous agent to follow a route, enable the autonomous agent toimplement a non-generic fallback plan and/or set of control commandswhich is configured for the current (and/or a very recent) environmentand/or behavior/action of the agent (e.g., approaching a left turn at anintersection, driving in a right lane of a freeway, approaching a stopsign, etc.), and/or can perform any other suitable functions. S230 ispreferably performed via a drive-by-wire control system of the agent,but can additionally or alternatively be performed with any suitablecontrol system(s).

S230 can include selectively operating the system (and/or a low-levelsafety platform thereof) between a primary mode (e.g., in which theprimary plan is implemented) and a fallback mode (e.g., in which afallback plan is implemented) (transmitting a set of outputs to acommunication network based on the operating mode); wherein in theprimary mode S230 can facilitate operation of the vehicle based on theprimary plan (e.g., determined in S210); wherein in the fallback modeS230 facilitate operation the vehicle based on the stored fallback plan.

S230 can include transitioning between a primary mode and a fallbackmode based on a determination of one or more fallback conditions, suchas by a watchdog (a.k.a. validation module) of the low-level safetyplatform. A fallback condition be determined in response to detecting anabsence of inputs from the computing system (e.g., after an expectedtime of arrival has passed, after a predetermined time period haspassed, etc.), which can indicate that the computing system has failed.Additionally or alternatively, a fallback condition can be determinedbased on an active determination that the computing system has failed(e.g., from a sensor system and/or set of health monitors, in responseto an alert and/or message, etc.; via a watchdog monitoring the healthof the outputs 104 and/or 106) and/or its outputs cannot be trusted, inresponse to determining that another system component (e.g., sensorsystem) has failed, in response to detecting one or more datainconsistencies (e.g., in an upstream data source), in response to aparticular situation and/or environment (e.g., emergency vehicle such asambulance and/or fire engine detected nearby, unusual situations such asfailures and/or edge cases, etc.), and/or at any other suitable timesand/or in any other motivating conditions for the agent to reach aminimum risk condition.

In variants, instructions (e.g., a trajectory, a set of waypoints, etc.)corresponding to the primary plan or fallback plan (e.g., generated bythe computing system, the low-level safety platform, etc.) can betransmitted to a control system (e.g., drive-by-wire system) of theagent and/or to any other suitable components of the system. Theinstructions are preferably either a plan associated with the 1^(st) setof outputs 104 (e.g., primary plan, trajectory, etc.) or the 2^(nd) setof outputs 106 (e.g., fallback plan, set of waypoints, etc.), but canadditionally or alternatively include a different plan (e.g., a modifiedtrajectory determined based on a trajectory of the 2nd set of outputs),other control commands, other information, and/or any other suitableinstructions to navigate the ego vehicle.

In one set of variants, an example of which is shown in FIG. 8, S230 caninclude: detecting that communication with the computing system ispresent (e.g., with a watchdog), and, in response, controlling thesystem in a primary operation mode by transmitting instructions (e.g., amost recent trajectory) of the 1^(st) set of outputs to thecommunication network, which functions to provide the control system(via the communication network) with normal operational trajectoriesalong a route of the agent when the computing system is operational.S230 can additionally include, detecting that communication with thecomputing system is lost, and, in response controlling the system in afallback operation mode by transmitting instructions (e.g., set ofwaypoints, etc.) associated with the 2^(nd) set of outputs (e.g.,fallback plan) to the communication network, which functions to providethe control system with a trajectory and/or positions (e.g., waypoints)and/or control commands configured to operate the vehicle safely (e.g.,to pull over, to continue driving if the vehicle cannot pull over, tofinish an action and then pull over and/or stop, to safely operate untilthe computing system is operational, to reach a safe state/navigationaledge, etc.) in an event of an emergency (e.g., computing systemfailure). Additionally or alternatively, S230 can function to ensurethat a selected output of the 2^(nd) set of outputs is safe to implement(e.g., based on its associated expiration parameter, based on anobstacle detection routine, etc.), and/or can perform any other suitablefunctions.

In some variants, facilitating operation in the fallback mode canoptionally include: checking the expiration parameter of the most recent2^(nd) output (e.g., set of waypoints, set of control commands, safetytrajectory, etc.), wherein if the most recent 2^(nd) output is notexpired, the corresponding trajectory is passed to the communicationnetwork.

Additionally or alternatively, S230 can optionally include validatingthe 2^(nd) output (e.g., based on sensor inputs to ensure that the agentdoes not encounter an obstacle), modifying the 2^(nd) output (e.g.,based on the sensor inputs), generating a new set of waypoints and/ortrajectory and/or set of control commands (e.g., based on the sensorinputs, in an event that all safety trajectories are expired, etc.),and/or any other processes.

Additionally or alternatively, facilitating operation in the fallbackmode can include selecting from multiple viable 2^(nd) output options(e.g., multiple safety trajectories in an event that multipletrajectories are not expired) and/or any other suitable processes.

Further additionally or alternatively, facilitating operation in thefallback mode can include: triggering any other processes, such astriggering the receipt of a set of sensor inputs (e.g., as describedabove). In some variations, for instance, in response to detecting thatthe computing system has failed, the low-level safety platform cantrigger sensor inputs normally received at the computing system and/or asubset of these sensor inputs (e.g., a partial subset of LIDAR sensors,sensors from only a relevant view of the agent, etc.) to be additionallyreceived and/or diverted to the low-level safety platform. Additionallyor alternatively, S230 can be performed in absence of sensor inputs,have already received sensor inputs (e.g., continuously receiving sensorinputs), and/or S230 can include any other suitable processes.

S230 can additionally or alternatively include any other suitableprocesses and/or be otherwise suitably performed.

However, the method can include any other suitable elements and/or canotherwise facilitate fallback planning and control.

4.5 Method—Variations

In a first variation of the method 200 (e.g., as shown in FIG. 2, asshown in FIGS. 3A-3B, etc.), the method includes: receiving, at alow-level safety platform, a set of inputs from a computing system,wherein the set of inputs includes a 2^(nd) output (e.g., a safetytrajectory, a set of waypoints configured to reach a nearestnavigational edge, a set of raw actuator control commands determinedbased on the set of waypoints, etc.), and optionally an expirationparameter associated with the 2^(nd) output; storing the 2^(nd) outputand its expiration parameter; in an event that communication with thecomputing system is lost and/or another trigger is initiated,transmitting a 2^(nd) output to the communication network; and operatingthe autonomous agent based on the output transmitted to a control system(e.g., drive-by-wire control system) via the communication network.

In a second variation of the method 200 (e.g., as shown in FIG. 2, asshown in FIGS. 3A-3B, etc.), the method includes: receiving, at alow-level safety platform, a set of inputs from a computing system,wherein the set of inputs includes a 1^(st) output (e.g., an operationaltrajectory), a 2^(nd) output (e.g., a safety trajectory, a set of rawactuator control commands, etc.), and an expiration parameter associatedwith the 2^(nd) output; storing the 2^(nd) output and its expirationparameter; in an event that communication with the computing system ispresent, transmitting a 1^(st) output to the communication platform; inan event that communication with the computing system is lost and/oranother trigger is initiated, transmitting a 2^(nd) output to thecommunication network; and operating the autonomous agent based on theoutput transmitted to a control system (e.g., drive-by-wire controlsystem) via the communication network.

Alternative embodiments implement the above methods and/or processingmodules in non-transitory computer-readable media, storingcomputer-readable instructions. The instructions can be executed bycomputer-executable components integrated with the computer-readablemedium and/or processing system. The computer-readable medium mayinclude any suitable computer readable media such as RAMs, ROMs, flashmemory, EEPROMs, optical devices (CD or DVD), hard drives, floppydrives, non-transitory computer readable media, or any suitable device.The computer-executable component can include a computing system and/orprocessing system (e.g., including one or more collocated ordistributed, remote or local processors) connected to the non-transitorycomputer-readable medium, such as CPUs, GPUs, TPUS, microprocessors, orASICs, but the instructions can alternatively or additionally beexecuted by any suitable dedicated hardware device.

Embodiments of the system and/or method can include every combinationand permutation of the various system components and the various methodprocesses, wherein one or more instances of the method and/or processesdescribed herein can be performed asynchronously (e.g., sequentially),contemporaneously (e.g., concurrently, in parallel, etc.), or in anyother suitable order by and/or using one or more instances of thesystems, elements, and/or entities described herein. Components and/orprocesses of the following system and/or method can be used with, inaddition to, in lieu of, or otherwise integrated with all or a portionof the systems and/or methods disclosed in the applications mentionedabove, each of which are incorporated in their entirety by thisreference.

As a person skilled in the art will recognize from the previous detaileddescription and from the figures and claims, modifications and changescan be made to the preferred embodiments of the invention withoutdeparting from the scope of this invention defined in the followingclaims.

We claim:
 1. An autonomous fallback method for a vehicle, comprising:receiving an environmental representation which identifies a set ofdynamic agents; based on the environmental representation, determining aset of navigational edge candidates; classifying a navigational edgecandidate of the set of navigational edge candidates as available basedon an occupancy prediction corresponding to each dynamic agent of theset of dynamic agents, wherein each dynamic agent is assumed to bestatic within a planning horizon; based on the classification of thenavigational edge candidate as available, generating a fallback planbased on the navigational edge candidate; storing the fallback plan at amemory coupled to an embedded controller; and determining satisfactionof a trigger condition and, in response, autonomously controlling thevehicle based on the fallback plan with the embedded controller.
 2. Theautonomous fallback method of claim 1, further comprising: updating thefallback plan based on a satisfaction of an expiration conditionassociated with the fallback plan.
 3. The autonomous fallback method ofclaim 2, wherein the expiration condition comprises a detection that asecond fallback plan associated with a second point in time has beengenerated, wherein the second point in time is later than a first pointin time associated with the fallback plan.
 4. The autonomous fallbackmethod of claim 1, while autonomously controlling the vehicle based onthe fallback plan: with vehicle odometry, traversing a path towards thenavigational edge candidate; detecting an obstacle along the path of thevehicle; and executing a full stop based on the obstacle detection. 5.The autonomous fallback method of claim 1, wherein the availableclassification of the navigational edge candidate is based on a map. 6.The autonomous fallback method of claim 1, wherein the set ofnavigational edge candidates are determined at a predetermined set ofdistances relative to an ego-vehicle position.
 7. The autonomousfallback method of claim 1, further comprising: selecting thenavigational edge from a plurality of available navigational edges basedon a kinematic constraint.
 8. The autonomous fallback method of claim 1,further comprising: selecting the navigational edge from a plurality ofavailable navigational edges based on a minimum distance constraint. 9.The autonomous fallback method of claim 1, wherein the fallback plan isdetermined with an autonomous computing system of the vehicle, whereinthe trigger condition comprises a communication failure of theautonomous computing system.
 10. The autonomous fallback method of claim9, wherein the embedded controller is within a low-level safety platformwhich is communicatively connected to the autonomous computing systemand a vehicle communication network.
 11. The autonomous fallback methodof claim 1, further comprising: prior to determining satisfaction of thetrigger condition, autonomously controlling the vehicle based on aprimary plan, wherein the primary plan is determined based on theenvironmental representation.
 12. The autonomous fallback method ofclaim 11, wherein the primary plan and fallback plan are each determinedwith a high-level planner, wherein the embedded controller is within alow-level safety platform, wherein the trigger condition comprises acommunication failure between the high-level planner and the low-levelsafety platform.
 13. The autonomous fallback method of claim 1, whereinthe set of dynamic agents comprises an automobile.
 14. A method for avehicle, comprising: determining an environmental representationcomprising a set of dynamic agents; determining a set of navigationcandidates based on the environmental representation; classifying anavigation candidate based on an occupancy prediction corresponding toeach dynamic agent, wherein the occupancy prediction for each dynamicagent is fixed within a planning horizon; based on the classification ofthe navigation candidate, generating a fallback plan based on thenavigation candidate; determining satisfaction of a trigger condition;and in response to determining satisfaction of the trigger condition,autonomously controlling the vehicle with the embedded controller basedon the fallback plan.
 15. The method of claim 14, further comprising:updating the fallback plan based on a satisfaction of an expirationcondition associated with the fallback plan.
 16. The method of claim 14,further comprising, while autonomously controlling the vehicle, based onthe fallback plan: traversing a path towards the navigation candidate;and executing a full stop based on an obstacle detection along the path.17. The method of claim 14, wherein the set of navigation candidates aredetermined at a predetermined set of distances relative to anego-vehicle position.
 18. The method of claim 14, further comprising:selecting the navigation candidate from a plurality of availablenavigation candidates based on a kinematic constraint or a minimumdistance constraint.
 19. The method of claim 14, wherein the fallbackplan is determined with an autonomous computing system of the vehicle,wherein the trigger condition comprises a communication failure of theautonomous computing system.
 20. The method of claim 14, wherein theembedded controller is within a low-level safety platform which iscommunicatively connected to the autonomous computing system and avehicle communication network.